Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis

Seguro, J.V; Lambert, T.W

doi:10.1016/s0167-6105(99)00122-1

Cited by 798 publications

(435 citation statements)

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“…By using a Weibull distribution to describe the probability function of the instantaneous wind speed as a function of the average wind speed [Wu and Chiu, 1983;Seguro and Lambert, 2000], which in turn depends on the average shear velocity, Kok [2010] obtained an approximate expression for the probability that saltation transport takes place at any given moment:…”

Section: Introductionmentioning

confidence: 99%

An improved parameterization of wind‐blown sand flux on Mars that includes the effect of hysteresis

Kok

2010

Geophysical Research Letters

111

210

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[1] Saltation, the wind-driven hopping motion of sand grains, forms dunes and ripples, and ejects fine dust particles into the atmosphere on Mars. Although the wind speed at which saltation is initiated, the "fluid threshold," has been studied extensively, the wind speed at which saltation is halted, the "impact threshold," has been poorly quantified for Mars conditions. I present an analytical model of the impact threshold, which is in agreement with measurements and numerical simulations for Earth conditions. For Mars conditions, the impact threshold is approximately an order of magnitude below the fluid threshold, in agreement with previous studies. Saltation on Mars can thus be sustained at wind speeds an order of magnitude less than required to initiate it, leading to the occurrence of hysteresis. I include the effect of hysteresis into an improved parameterization of sand transport on Mars.

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Section: Introductionmentioning

confidence: 99%

An improved parameterization of wind‐blown sand flux on Mars that includes the effect of hysteresis

Kok

2010

Geophysical Research Letters

111

210

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“…This includes, for example, the modelling of insurance claims resulting from natural catastrophe events in the US (Chernobai et al, 2006), losses from operational risks in the banking industry (Chernobai et al, 2010), reinsurance premiums based on fire loss data (Cummins et al, 1990), the modelling of extreme returns from investments (Goncu et al, 2012) and wind speed frequency distributions for use in the analysis of wind energy potentials (Justus et al, 1976;Stevens and Smulders, 1979;Seguro and Lambert, 2000). The Weibull distribution is defined by a shape parameter , and a scale parameter , and is denoted by the following CDF:…”

Section: The Weibull Distributionmentioning

confidence: 99%

Quantifying catastrophic and climate impacted hazards based on local expert opinions

Keighley

Longden

Mathew

et al. 2018

Journal of Environmental Management

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The analysis of catastrophic and climate impacted hazards is a challenging but important exercise, as the occurrence of such events is usually associated with high damage and uncertainty. Often, at the local level, there is a lack of information on rare extreme events, such that available data is not sufficient to fit a distribution and derive parameter values for the frequency and severity distributions. This paper discusses local assessments of extreme events and examines the potential of using expert opinions in order to obtain values for the distribution parameters. In particular, we illustrate a simple approach, where a local expert is required to only specify two percentiles of the loss distribution in order to provide an estimate for the severity distribution of climate impacted hazards. In our approach, we focus on so-called heavy-tailed distributions for the severity, such as the Lognormal, Weibull and Burr XII distribution. These distributions are widely used to fit data from catastrophic events and can also represent extreme losses or the so-called tail of the distribution. An illustration of the method is provided utilising an example that quantifies the risk of bushfires in a local area in Northern Sydney.

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“…where A and k are respectively the scale and the shape parameters of Weibull, which can be determined by using the Maximum likelihood method equations (3) and (4) [17] …”

Section: -P2mentioning

confidence: 99%

“…It represents the fraction of the total energy delivered over a period (E out ) divided by the maximum energy that could have been delivered if the wind turbine was used at maximum capacity over the all period. For the period of a year, the maximum energy output can be calculated by [17]:…”

Section: -P3mentioning

confidence: 99%

Economic analysis of wind electricity generation in the northern coast of Senegal

Bilal

Kébé

Sabourin

et al. 2014

Int. J. Metrol. Qual. Eng.

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Abstract. The main target of this paper is to determine the seasonal features of the wind speed in the northwestern of Senegal and to assess the cost of annual energy output using wind turbines provided from several manufactures. The wind data used for this study were collected in four sites located in the northwestern coast of Senegal and cover the period of one year. The seasonal mean wind speed and the annual distribution curves were obtained by using the Weibull distribution function. A technical assessment of electricity generation from five big wind turbines which power is between 640 kW and 2000 kW and from six small wind turbines which power is between 250 W and 10 kW were carried out. The results obtained show that the lower cost (0.0265 e/kWh in Sokhar) was observed for the wind turbine Ecotècnia 62. The highest cost was 0.5103 e/kWh (in Gandon) observed for the technology of Alize. All wind turbines was better suited to the site of Sokhar with the highest capacity factor and the lowest cost of output energy.

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Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis

Cited by 798 publications

References 1 publication

An improved parameterization of wind‐blown sand flux on Mars that includes the effect of hysteresis

An improved parameterization of wind‐blown sand flux on Mars that includes the effect of hysteresis

Quantifying catastrophic and climate impacted hazards based on local expert opinions

Economic analysis of wind electricity generation in the northern coast of Senegal

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